Memristors with Monolayer Graphene Electrodes Grown Directly on Sapphire Wafers

The development of the memristor has generated significant interest due to its non-volatility, simple structure, and low power consumption. Memristors based on graphene offer atomic monolayer thickness, flexibility, and uniformity and have attracted attention as a promising alternative to contemporary field-effect transistor (FET) technology in applications such as logic and memory devices, achieving higher integration density and lower power consumption. The use of graphene as electrodes in memristors could also increase robustness against degradation mechanisms, including oxygen vacancy diffusion to the electrode and unwanted metal ion diffusion. However, to realize this technological transformation, it is necessary to establish a scalable, robust, and cost-effective device fabrication process. Here we report the direct growth of high-quality monolayer graphene on sapphire wafers in a mass-producible, contamination-free, and transfer-free manner, using a commercially available metal-organic chemical vapor deposition (MOCVD) system. By taking advantage of this approach, graphene-electrode based memristors are developed, and all the processes used in the device fabrication incorporating graphene electrodes can be performed at wafer scale. The graphene electrode-based memristor demonstrates promising characteristics in terms of endurance, retention, and ON/OFF ratio. This work presents a possible and viable route to achieving robust graphene-based memristors in a commercially and technologically sustainable manner, paving the way for the realization of more powerful and compact integrated graphene electronics in the future.

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Publication Details

Journal
ACS Applied Electronic Materials
Published
2024-09-16
DOI
https://doi.org/10.1021/acsaelm.4c01208
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.04

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article

Memristors with Monolayer Graphene Electrodes Grown Directly on Sapphire Wafers

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ACS Applied Electronic Materials
Advanced Memory and Neural Computing
1.04
article

Memristors with Monolayer Graphene Electrodes Grown Directly on Sapphire Wafers

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article en
5 citations

Abstract

The development of the memristor has generated significant interest due to its non-volatility, simple structure, and low power consumption. Memristors based on graphene offer atomic monolayer thickness, flexibility, and uniformity and have attracted attention as a promising alternative to contemporary field-effect transistor (FET) technology in applications such as logic and memory devices, achieving higher integration density and lower power consumption. The use of graphene as electrodes in memristors could also increase robustness against degradation mechanisms, including oxygen vacancy diffusion to the electrode and unwanted metal ion diffusion. However, to realize this technological transformation, it is necessary to establish a scalable, robust, and cost-effective device fabrication process. Here we report the direct growth of high-quality monolayer graphene on sapphire wafers in a mass-producible, contamination-free, and transfer-free manner, using a commercially available metal-organic chemical vapor deposition (MOCVD) system. By taking advantage of this approach, graphene-electrode based memristors are developed, and all the processes used in the device fabrication incorporating graphene electrodes can be performed at wafer scale. The graphene electrode-based memristor demonstrates promising characteristics in terms of endurance, retention, and ON/OFF ratio. This work presents a possible and viable route to achieving robust graphene-based memristors in a commercially and technologically sustainable manner, paving the way for the realization of more powerful and compact integrated graphene electronics in the future.

ACS Applied Electronic MaterialsVol. 6(10)
Queen Mary University of London (GB)
Queen Mary University of London, Royal Society, Innovate UK
Affordable and clean energy
Openalex Percentile: Top 23%
Advanced Memory and Neural Computing
1.04
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